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2. Story Fragment Stitching: The Case of the Story of Moses

   Aldawsari, Mohammed; Asgari, Ehsaneddin; Finlayson, Mark A. (January 2021, 1st Workshop on Artificial Intelligence for Narratives (AI4N 2020))

   null (Ed.)

   We introduce the task ofstory fragment stitching,which is the process of automatically aligning andmerging event sequences of partial tellings of astory (i.e.,story fragments). We assume that eachfragment contains at least one event from the storyof interest, and that every fragment shares at leastone event with another fragment. We propose agraph-based unsupervised approach to solving thisproblem in which events mentions are representedas nodes in the graph, and the graph is compressedusing a variant of model merging to combine nodes.The goal is for each node in the final graph to con-tain only coreferent event mentions. To find coref-erent events, we use BERT contextualized embed-ding in conjunction with atf-idfvector representa-tion. Constraints on the merge compression pre-serve the overall timeline of the story, and the finalgraph represents the full story timeline. We evalu-ate our approach using a new annotated corpus ofthe partial tellings of the story of Moses found inthe Quran, which we release for public use. Ourapproach achieves a performance of 0.63F1score 

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3. The Cosmic Lithium Story

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   Lithium’s story spans the history of the universe and is one that links to all its largest-scale processes: big bang nucleosyntheses, the evolution of stars, and galactic chemical evolution. Lithium was the only metal produced in the big bang, alongside the gases H and He. Stars destroy both stable isotopes of Li easily, yet we still have Li today, even after generations of stars have come and gone. Ongoing production of Li by galactic cosmic rays and by a limited number of Li-producing nuclear reactions and transport processes in some rare types of stars keeps lithium present in the universe. 

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4. Building community heat action plans story by story: A three neighborhood case study

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5. Adversarial Strong Story Experience Management

   https://doi.org/10.1609/aiide.v21i1.36828  

   Genoese-Zerbi, Valentina; Robertson, Justus (November 2025, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   In strong story experience management problems, an automated storytelling agent balances player autonomy with narrative structure in the context of an interactive story game world. However, it is possible for the game world to get softlocked in states outside narrative structures specified by the game designer. These states are called dead-ends. In this paper, we revisit adversarial strong story experience management, a framing of the experience management problem that models interactive storytelling as an adversarial game where dead-ends are losses. This framing is adversarial against narrative softlocks, not necessarily the player. We present a novel agent based on adversarial search and deep reinforcement learning, which is trained to avoid dead-ends while preserving player autonomy. We compare our approach to a reactive, narrative plan-based mediation system on a test set of games compatible with current narrative planning techniques. We show that our adversarial architecture outperforms narrative mediation on a suite of dead-end metrics during game trace and breadth-first tests of state transition system exploration, using classical and intentional planning domains. 

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